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Performant Security for LLMs

Data and AI hold immense potential for businesses, but can also introduce privacy and security risks. NVIDIA was the first GPU to deliver Confidential Computing on the NVIDIA Hopper™ architecture with the unprecedented acceleration of NVIDIA Tensor Core GPUs. NVIDIA Blackwell architecture has taken Confidential Computing to the next level with nearly identical performance compared to unencrypted modes for large language models (LLMs) - providing the ability to uncover revolutionary insights with confidence that data and models remain secure, compliant, and uncompromised.

The Benefits of NVIDIA Confidential Computing

Hardware-Based Security and Isolation

Maintain compliance while protecting the confidentiality and integrity of data while achieving full isolation of the virtual machine from edge to cloud with NVIDIA Blackwell. Confidential Computing provides a physically isolated trusted execution environment (TEE) to secure the entire workload while data is in use.

Performant Security Choices

Confidential Computing on NVIDIA Blackwell brings together performance and security for even the largest data models to provide protection of data, AI models, and applications in use. Confidential Computing is available on NVIDIA Blackwell and Hopper GPUs.

Verifiability with Device Attestation

Support a zero-trust architecture with attestation services that verify the trustworthiness of compute assets. Maintain compliance and ensure that apps and data are protected within the TEE with NVIDIA Blackwell and Hopper GPUs, regardless of where the platform or workload is running.

Performance Without Code Changes

Leverage all the benefits of Confidential Computing without code changes. NVIDIA’s GPU-optimized software accelerates end-to-end AI workloads on Blackwell and Hopper GPUs, allowing organizations to maintain privacy, security, and regulatory compliance.

Unlock New Possibilities for AI Security

Protect AI Intellectual Property

NVIDIA Confidential Computing preserves the confidentiality and integrity of AI models and algorithms that are deployed on Blackwell and Hopper GPUs. Independent software vendors (ISVs) can distribute and deploy their proprietary AI models at scale on shared or remote infrastructure from edge to cloud.

Security for AI Training and Inference

AI models such as LLMs can pose privacy and data security risks when trained on private data collected from customers or generated from business operations. These risks are compounded when personally identifiable information (PII) and personal information (PI) are included in training models. Keep data secure with Confidential Computing powered by NVIDIA Blackwell, and ensure data is protected against exposure and breaches.

Secure Multi-Party Collaboration

Building and improving AI models for use cases like fraud detection, medical imaging, and drug development requires diverse, carefully labeled datasets for training neural networks. This demands collaboration between multiple parties without compromising the confidentiality and integrity of the data sources. NVIDIA Confidential Computing unlocks secure multi-party computing, letting organizations work together to train or evaluate AI models and ensures that both data and the AI models are protected from unauthorized access, external attacks, and insider threats at each participating site.

 

Take a Deep Dive into the NVIDIA Hopper Architecture

Confidential Compute on NVIDIA Hopper H100 Read Whitepaper